Forward and inverse kinematics solution of a robotic manipulator using a multilayer feedforward neural network

• Autorzy: Sharkawy Abdel-Nasser

Identyfikatory

DOI

10.30464/jmee.00300

• Języki publikacji: EN

Abstrakty

EN

In this paper, a multilayer feedforward neural network (MLFFNN) is proposed for solving the problem of the forward and inverse kinematics of a robotic manipulator. For the forward kinematics solution, two cases are presented. The first case is that one MLFFNN is designed and trained to find solely the position of the robot end-effector. In the second case, another MLFFNN is designed and trained to find both the position and the orientation of the robot end-effector. Both MLFFNNs are designed considering the joints’ positions as the inputs. For the inverse kinematics solution, a MLFFNN is designed and trained to find the joints’ positions considering the position and the orientation of the robot end-effector as the inputs. For training any of the proposed MLFFNNs, data is generated in MATLAB using two different cases. The first case is that data is generated assuming an incremental motion of the robot’s joints, whereas the second case is that data is obtained with a real robot considering a sinusoidal joint motion. The MLFFNN training is executed using the Levenberg-Marquardt algorithm. This method is designed to be used and generalized to any DOF manipulator, particularly more complex robots such as 6-DOF and 7-DOF robots. However, for simplicity, this is applied in this paper using a 2-DOF planar robot. The results show that the approximation error between the desired output and the estimated one by the MLFFNN is very low and it is approximately equal to zero. In other words, the MLFFNN is efficient enough to solve the problem of the forward and inverse kinematics, regardless of the joint motion type.

Słowa kluczowe

EN

multilayer neural network; feedforward neural network; forward kinematics; inverse kinematics; 2-DOF planar robot; Levenberg-Marquardt algorithm; generated data

PL

sieci neuronowe; sieci neuronowe jednokierunkowe; sieci neuronowe wielowarstwowe; kinematyka prosta; kinematyka odwrotna; algorytm Levenberga-Marquardta; generowanie danych

• Wydawca: Wydawnictwo Uczelniane Politechniki Koszalińskiej
• Czasopismo: Journal of Mechanical and Energy Engineering
• Rocznik: 2022
• Tom: Vol. 6, No 2
• Strony: 1--17
• Opis fizyczny: Bibliogr. 51 poz., rys., wykr.

Twórcy

autor

Sharkawy Abdel-Nasser

• Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena, 83523, Egypt
• Mechanical Engineering Department, College of Engineering, Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).